Recently, transformers [1]} have shown promising results on several vision tasks, including VIS [2]}, [3]}. The recent transformer-based VIS approaches [2]}, [3]} are built on DETR [6]} and Deformable DETR [7]} frameworks, utilizing an encoder-decoder architecture along with instance sequence matching and segmentation mechanisms to generate final video mask predictions. These approaches typically employ either single-scale spatio-temporal features [2]} or per-frame multi-scale features [3]} during attention computations at the encoder and decoder. However, such an attention computation ignores the multi-scale spatio-temporal feature relationships, which are crucial towards handling target appearance deformations due to real-world challenges such as, scale variation, change in aspect-ratio and fast motion in videos.
